Predictive maintenance of refrigeration cases

ABSTRACT

Embodiments of the present invention disclose a method, a computer program product, and a computer system for predictive maintenance of refrigeration cases. A computer collects a temperature time series for a refrigeration case and, based on the temperature time series, learns a refrigeration case signature for both non-frost and defrost cycles. The computer generates features based on the refrigeration case signature and compares the refrigeration case signature to real time, or observed, temperatures and features using a rule-based and/or machine learning framework. Based on determining that the real time data varies beyond a threshold from the refrigeration case signature, the computer identifies a failure symptom of the refrigeration case and diagnoses a root cause of the symptom or failure. In addition, the computer may activate an alarm and open a work order corresponding to the root cause of the symptom or failure.

BACKGROUND

The present invention relates to predictive maintenance, and moreparticularly to predictive maintenance of refrigeration cases.Refrigeration cases are relied on to maintain temperatures inapplications such as food storage. Considering that even a brief failureof a refrigeration case can result in the spoilage of large amounts offood and money, refrigeration cases often require constant supervisionto ensure proper function. Interestingly, many refrigeration casesymptoms indicative of a failure can be foreseen prior to failure withproper supervision. This constant supervision, however, comes at aprice. Constant supervision of large amounts of refrigeration cases iscostly in terms of both hardware and human resources. Moreover, it isalso common to have false alarms due to overly sensitive supervision,resulting in further added costs.

SUMMARY

Embodiments of the present invention disclose a method, a computerprogram product, and a computer system for predictive maintenance ofrefrigeration cases. A computer collects a temperature time series for arefrigeration case and, based on the temperature time series, learns arefrigeration case signature for both non-frost and defrost cycles. Thecomputer generates features based on the refrigeration case signatureand compares the refrigeration case signature to real time, or observed,temperatures and features using a rule-based and/or machine learningframework. Based on determining that the real time data varies beyond athreshold from the refrigeration case signature, the computer identifiesa failure symptom of the refrigeration case and diagnoses a root causeof the symptom or failure. In addition, the computer may activate analarm and open a work order corresponding to the root cause of thesymptom or failure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic diagram of a predictive maintenance system 100, inaccordance with an embodiment of the present invention.

FIG. 2A is a schematic diagram illustrating the operations of predictivemaintenance program 122 of predictive maintenance system 100 inpredicting maintenance and identifying failure symptoms of refrigerationcase 110, in accordance with an embodiment of the present invention.

FIG. 2B is a schematic diagram illustrating the operations of predictivemaintenance program 122 of predictive maintenance system 100 inpredicting maintenance and identifying failure symptoms of refrigerationcase 110, in accordance with an embodiment of the present invention.

FIG. 3 illustrates a temperature time series of refrigeration case 110,in accordance with an embodiment of the present invention.

FIG. 4 illustrates expected and anomalous defrost cycles, in accordancewith an embodiment of the present invention.

FIG. 5 illustrates valid and invalid defrost cycles of refrigerationcase 110, in accordance with an embodiment of the present invention.

FIG. 6 illustrates a temperature time series having valid and invaliddefrost cycles based on refrigeration case 110 set temperature precedingand subsequent to defrosting, in accordance with an embodiment of thepresent invention.

FIG. 7 illustrates a homogenous cluster of defrost cycles, in accordancewith an embodiment of the present invention.

FIG. 8 illustrates a continuous cluster of defrost cycles, in accordancewith an embodiment of the present invention.

FIG. 9 illustrates a non-continuous cluster of defrost cycles, inaccordance with an embodiment of the present invention.

FIG. 10 illustrates a non-defrost temperature time series broken downinto seasonal, trend, and random components, in accordance with anembodiment of the present invention.

FIG. 11 is a block diagram depicting the hardware components ofpredictive maintenance system 100 of FIG. 1, in accordance with anembodiment of the invention.

FIG. 12 depicts a cloud computing environment, in accordance with anembodiment of the present invention.

FIG. 13 depicts abstraction model layers, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION

A predictive maintenance system 100 in accordance with an embodiment ofthe invention is illustrated by FIG. 1. In the example embodiment,refrigeration case 110 performs defrost cycles periodically to preventice build-up on coils. During the defrost cycle, refrigeration case 110temperatures typically follow a regular pattern, constituting arefrigeration case 110 signature. Deviation from this signature is oftena symptom of an underlying issue or change in refrigeration case 110state. In addition, issues can be detected far in advance by trackingdefrost cycles and, what's more, certain issues manifest only duringdefrost cycles, thereby making it advantageous to track defrost cyclesin addition to non-defrost cycles. The present invention leverages theuse of the signature, as well as other features, in order to monitor thehealth of refrigeration case 110, predict the required maintenance ofrefrigeration case 110, identify failure symptoms of refrigeration case110, and diagnose failures of refrigeration case 110.

In the example embodiment, network 108 is a communication channelcapable of transferring data between connected devices. In the exampleembodiment, network 108 may be the Internet, representing a worldwidecollection of networks and gateways to support communications betweendevices connected to the Internet. Moreover, network 108 may include,for example, wired, wireless, or fiber optic connections which may beimplemented as an intranet network, a local area network (LAN), a widearea network (WAN), or any combination thereof. In further embodiments,network 108 may be a Bluetooth network, a WiFi network, or a combinationthereof. In yet further embodiments, network 108 may be atelecommunications network used to facilitate telephone calls betweentwo or more parties comprising a landline network, a wireless network, aclosed network, a satellite network, or any combination thereof. Ingeneral, network 108 can be any combination of connections and protocolsthat will support communications between refrigeration case 110, server120, and other electronic or mechanical devices.

In the example embodiment, refrigeration case 110 includes sensor 112and is a refrigeration and cold storage system (RCSS) used for modifyingthe temperature of an area known as a case. For example, refrigerationcase 110 may include refrigerators, coolers, freezers, air conditioners,and heat pumps. In the example embodiment, refrigeration case 110 isutilized to reduce temperature of the case, i.e. cool, and operates intwo cycles: a non-defrosting (operational) cycle in which a lowoperational temperature is maintained and a defrosting cycle in whichthe temperature is increased to melt ice that is built up onrefrigeration case 110 coils. Moreover, refrigeration case 110 iscapable of communicating with network 108 and other devices via meanssuch as a local area connection (LAN), a Wi-Fi connection, a Bluetoothconnection, an infrared connection, a near field communication (NFC), orother communication methods.

In the example embodiment, sensor 112 is an electronic component capableof measuring and detecting events or changes in the state of variousmetrics within refrigeration case 110. Sensor 112 may be a thermometer,humidity sensor, pressure sensor/barometer, power consumption meter of athe total system/individual components, revolutions persecond/minute/hour of mechanical components such as a compressor orpump, gyroscope, accelerometer, compass, global positioning system(GPS), proximity sensor, camera, microphone, light sensor, infraredsensor, weight sensor, or other device used for measuring an environmentor state. In the example embodiment, sensor 112 is located in the caseof refrigeration case 110 and referenced via network 108.

In the example embodiment, server 120 includes predictive maintenanceprogram 122 and may be a laptop computer, a notebook, a tablet computer,a netbook computer, a personal computer (PC), a desktop computer, apersonal digital assistant (PDA), a rotary phone, a touchtone phone, asmart phone, a mobile phone, a virtual device, a thin client, or anyother electronic device or computing system capable of receiving andsending data to and from other computing devices. While server 120 isshown as a single device, in other embodiments, server 120 may becomprised of a cluster or plurality of computing devices, workingtogether or working separately. Server 120 is described in more detailwith reference to FIG. 11.

Predictive maintenance program 122 is a software application capable ofcollecting a temperature time series corresponding to refrigeration case110 and extracting defrost temperature data from the temperature timeseries. Predictive maintenance program 122 is further capable offiltering anomalous data from the defrost temperature data andextracting a defrost temperature signature from the remaining defrosttemperature data. Moreover, predictive maintenance program 122 iscapable of determining a defrost anomaly threshold and defrost featurebased on the defrost temperature signature. In addition, predictivemaintenance program 122 is capable of extracting a non-defrost, oroperational, temperature signature the temperature time series andextracting non-defrost features from the non-defrost temperaturesignature. Predictive maintenance program 122 is further capable ofextracting additional features from the data and a case type feature.Moreover, predictive maintenance program 122 is capable of measuringreal time, or observed, data and identifying a failure symptom based oncomparing the observed data to the temperature signatures. Based onidentifying a failure symptom, predictive maintenance is capable ofdiagnosing a root cause of the symptom or failure and opening a workorder to address the root cause. Lastly, predictive maintenance program122 is capable of closing the work order upon rectification of the rootcause responsible for the symptom or failure.

FIGS. 2A and 2B illustrate the operations of predictive maintenanceprogram 122 in identifying symptoms of a refrigeration case 110 failure.In the example embodiment, predictive maintenance program 122 utilizeseither or both a rule-based and machine learning framework foridentifying symptoms of a refrigeration case 110 failure, described ingreater detail herein.

Predictive maintenance program 122 first collects a temperature timeseries, or sequence of incremental temperature measurements,corresponding to the non-defrost and defrost cycles of refrigerationcase 110 (step 202). In the example embodiment, predictive maintenanceprogram 122 utilizes the temperature time series as historicaltemperature data to derive typical non-defrost and defrost temperaturepatterns, or signatures, as well as extract features therefrom. Usingthe temperature time series of refrigeration case 110, predictivemaintenance program 122 is capable of identifying metrics indicative ofthe most common causes of refrigeration case failure, including a faultydefrost profile, warming up of refrigeration case 110 that leads to anupward drift in temperature signal, increased variability in thetemperature signal, and a sudden change in the daily expectedtemperature profile over a very short period (e.g. 1-2 days). In theexample embodiment, predictive maintenance program 122 collects thetemperature time series of refrigeration case 110 from previouslycollected data by, for example, reference to a database of temperaturemeasurements. In other embodiments, predictive maintenance program 122may be configured to reference sensor 112 for temperature measurementsover the course of a threshold amount of non-defrost/defrost cycles, forexample one hundred cycles, or over a specified period of time, such asone hour, day, or month.

With reference to an illustrative example, FIG. 3 depicts a temperaturetime series corresponding to refrigeration case 110 wherein the y-axisindicates temperature (degreed Fahrenheit) and the x-axis indicatestime. Data points high in temperature represent defrost cycles whiledata points low in temperature represent non-defrost operation.

Predictive maintenance program 122 extracts defrost temperature datafrom the temperature time series (step 204). Refrigeration case 110 isperiodically defrosted to prevent ice build-up within the case byraising temperatures significantly higher than their normal operatingtemperatures for a specified duration. The specified defrost frequency,duration, and temperature are based on refrigeration case 110 type, forexample a chest freezer or beverage refrigerator. In any (refrigeration)case, however, defrost cycle temperatures for each case type aresomewhat consistent, constituting a defrost temperature signature.Accordingly, the present invention leverages, in either or both arule-based and machine learning framework, the consistency of thedefrost temperature signature to identify failure symptoms ofrefrigeration case 110 by identifying deviation of observed, i.e.real-time, data from the defrost signature. Not only is the defrosttemperature signature consistent, but also capable of exhibitingsymptoms of failure undetectable or only later detectable by simplymonitoring non-defrost, i.e. operational, temperatures. In the exampleembodiment, predictive maintenance program 122 extracts defrosttemperature data from the temperature time series by identifyingtemperature data having non-defrost and defrost flags indicative of whenthe data was sampled. Alternatively, predictive maintenance program 122may be configured to reference temperature data corresponding to timesat which non-defrost/defrost cycles are scheduled or configured to learna slope change that is indicative of a transition from a defrost to anon-defrost state and vice versa.

With reference again to FIG. 3 (step 204 continued), predictivemaintenance program 122 identifies data corresponding to defrost cyclesby identifying defrost flags associated with previously collected data.Alternatively, predictive maintenance program 122 may be trained toreference data during scheduled defrost cycles or trained to identify achange in slope indicative of a change to defrost cycles. To illustratethe principle that some symptom manifestations are only apparent whenmonitoring defrost temperatures, FIG. 3 depicts refrigeration case 110reaching the set temperature regularly during non-defrost cycles upuntil point B despite having abnormally low defrost temperaturesmeasuring ˜25% cooler than normal. Importantly, low defrost temperaturesare an indication that temperatures in refrigeration case 110 are notwarm enough for ice built up on the coils to melt off, likely leading topoor operational performance and/or subsequent failure. After failing toreach set temperature following point B, refrigeration case 110 isrepaired and functioning properly following point A. Notably, simplymonitoring non-defrost, i.e. operational, cycle temperatures would nothave identified the pending failure prior to point B because the settemperature was reached during operation and thus no alarm was raised.Conversely, monitoring and examination of defrost cycle temperatureswould have indicated well in advance of point B that a failure waspending, thereby saving money, time, and product.

Predictive maintenance program 122 filters out anomalous defrost cyclesfrom the defrost temperature data (step 206). Like outliers, anomalouscycles deviate from the typical defrost cycles, as depicted at a highlevel by FIG. 4 and with greater detail by FIG. 5, and are likely toskew the later determined defrost temperature signature. Accordingly,predictive maintenance program 122 removes anomalous cycles by, in theexample embodiment, eliminating data which exceed the upper and lowerbounds of refrigeration case 110 set point temperatures. In thisprocess, predictive maintenance program 122 considers refrigeration case110 operating temperature thirty minutes immediately prior to and postdefrost. If the average temperature during either time period is outsidethe upper or lower respective bounds, the intervening defrost cycle isconsidered anomalous and discarded.

With reference again to an illustrative example depicted FIG. 6,predictive maintenance program 122 identifies anomalous, or invalid,defrost cycles by comparing the adjoining non-defrost cycle temperaturesto the set point thirty minutes prior to and post defrost cycle. Here,data points collected after the case temperature exceeded the upperbound set point (horizontal line) are considered invalid and discardedas being anomalous.

Predictive maintenance program 122 extracts a defrost temperaturesignature from the remaining defrost temperature data (step 208). In theexample embodiment, predictive maintenance program 122 extracts thedefrost temperature signature based on the most prevalent patterns inthe remaining data. In order to extract the defrost temperaturesignature, predictive maintenance program 122 first determines whetherthe remaining temperature defrost data is homogenous by identifyingdominant clusters within the data. In the example embodiment, predictivemaintenance program 122 identifies clusters using hierarchicalclustering with Dynamic Time Warping (DTW) distance as the distancefunction. DTW is an algorithm used in time series analysis that measuressimilarities between two temporal sequences which may vary in speed. Ifpredictive maintenance program 122 determines that the set ishomogenous, clustering yields a single cluster containing all but a veryfew cycles. With reference to an example illustrated by FIG. 7,predictive maintenance program 122 identifies the depicted dominantcluster and utilizes the dominant cluster as a basis for the defrosttemperature signature. For case in which no dominant cluster emerges,predictive maintenance program 122 identifies one or more valid clustersof the data capable of contributing to the defrost temperaturesignature. In the example embodiment, the method for identifying the oneor more valid clusters depends on whether the cycles in the clusters arecontiguous in the time series, as depicted by an example in FIG. 8, ornon-continuous in time series, as depicted by an example in FIG. 9. Inthe case of contiguous clusters (FIG. 8), identifying valid clusterscomprises the additional step of dropping all contiguous clusters thatend with a work order. Then, the remaining cycles, contiguous or not,are used in constructing the defrost temperature signature. In theexample embodiment, predictive maintenance program 122 takes the medianof the cycles that remain as the defrost temperature signature. In otherembodiments, however, predictive maintenance program 122 may determine adefrost temperature signature alternatively.

Predictive maintenance program 122 determines an anomaly threshold fromthe defrost temperature signature (step 210). In the example embodiment,the anomaly threshold defines a maximum tolerable deviation of observeddata from the defrost temperature signature established above. Thisanomaly threshold may be used as a limit when implementing a rule-basedframework or, alternatively, may be used to define a feature, namely thedefrost anomaly score, when implementing a machine learning framework.In the rule-based framework, temperature data falling within the anomalythreshold is considered routine while temperature data falling outsideof the anomaly threshold is considered anomalous, i.e. a failure symptomof future refrigeration case 110. In the example embodiment, predictivemaintenance program 122 determines the anomaly threshold by computingthe DTW distance of each valid cycle from the defrost temperaturepattern and taking the 98^(th) percentile of the distances as thethreshold. When implementing a machine learning framework, predictivemaintenance program 122 may normalize the DTW distance of the anomalythreshold into one of six anomaly ranges, designated 0-6. In suchembodiments, predictive maintenance program 122 maintains a movingaverage of the anomaly range, which is designated as the feature defrostanomaly score (DAS). The rule-based and machine learning frameworks aredescribed in greater detail in the proceeding paragraphs.

Predictive maintenance program 122 extracts a non-defrost temperaturesignature from the temperature time series (step 212). Similar to theuse of the defrost temperature signature, predictive maintenance program122 utilizes the non-defrost temperature signature as a baseline foridentifying and quantifying deviations of observed, i.e. real time,data. These deviations are measured as a deviation score used incomputing whether a failure symptom is indicated by the data. In lessadvanced embodiments, predictive maintenance program 122 may define thenon-defrost temperature signature as the temperature time series as isand define the deviation score as a distance metric between an observedtemperature signature and the non-defrost temperature signature. In morecomplex embodiments, predictive maintenance program 122 may define thenon-defrost temperature signature as a value of a smoothed version ofthe temperature time series and define the deviation score as a functionof the change in value of the smoothed version over a given period time.

In the example embodiment, however (step 212 continued), predictivemaintenance program 122 defines the non-defrost temperature signature asa decomposed version of the temperature time series and defines thedeviation score as a result of a function applied to the resultingcomponents, such functions including a standard deviation, variance,skewness, exceedance beyond given ranges, etc. Specifically, in theexample embodiment predictive maintenance program 122 decomposes thetemperature time series using a seasonal and trend decomposition of timeseries by Loess (STL). The STL additively decomposes the measuredtemperature time series (Y_(t)) of a last k days into seasonal (S_(t)),trend (TO, and random (E_(t)) components, as depicted by FIG. 10 andsummarized by Equation 1:

Y _(t) =S _(t) +T _(t) +E _(t)  (1)

Predictive maintenance program 122 extracts features from thenon-defrost temperature pattern (step 214). Specifically, predictivemaintenance program 122 utilizes the resulting seasonal, trend, andrandom components in computing refrigeration case 110 features such as adrift score, a volatility score, and an anomaly score. The seasonalitycomponent (S_(t)) of the non-defrost temperature signal is primarily dueto the defrost activity that happens at several specific times per day(on average 2-6) and the daily/weekly store activity based on storeoperating hours and customer traffic patterns. Thus, as the currentlydiscussed features are concerned with non-defrost performance, theseasonal component is removed from the data and will be disregarded inthe following calculations. Predictive maintenance program 122 utilizesthe trend component (T_(t)) of the non-defrost temperature signature todetermine the feature drift score (D_(t)), or variation in trend day today, which for a given day (t) is defined by Equation 2 as:

D _(t) =T _(t) −T _(t−1)  (2)

In the example embodiment, the drift score feature represents adifference in the trend score of the current day (t) and the previousday (t−1).

Similarly, predictive maintenance program 122 utilizes the randomcomponent (E_(t)) of the non-defrost temperature signal to derive afeature for a volatility score (V_(t)), or average variability betweenconsecutively measured temperatures, where the volatility score for agiven day (t) is defined by Equation 3 as:

$\begin{matrix}{V_{t} = {\frac{1}{n}\Sigma_{1}^{n}{{E_{t} - E_{t - 1}}}}} & (3)\end{matrix}$

Here, E_(t) and E_(t−1) are computed based on the STL decomposition ofthe non-defrost temperature signal over n days, e.g. 21 days. In theexample embodiment, the volatility score for a given day is computed asthe average of the absolute values of the difference between the randomcomponent on a given day (E_(t)) and a day earlier (E_(t+1)) computedover the last n days. Restated, the volatility score provides a measureof the absolute change in the random component day-to-day, on anaverage, in the last n days.

In addition, predictive maintenance program 122 computes a feature foranomalies, expressed as an anomaly score. The anomaly score is used toaccount for any sudden changes in the daily temperature profile of agiven refrigeration case 110. In the example embodiment, the anomalyscore (A_(t)) for a given day (t) is the dynamic time warp (DTW)distance between the daily temperature profile (X_(t)) and the mediantemperature profile of the last m days (X_(t) ^(median)) as defined byEquation 4:

A _(t) =DTW(X _(t) ,X _(t) ^(median))  (4)

Here, the daily temperature profile (X_(t)) is computed as the timeseries of temperatures from the first to the final timestamp of a givenday. The median temperature profile (X_(t) ^(median)) is computed as thetime series of median temperatures over the last m days for eachtimestamp in a given day. Predictive maintenance program 122 operatesunder the assumption that the temperature at a given time-stamp of a dayis not expected to deviate much from the median temperature value at aclose enough timestamp in the last m days (e.g. 10). Based on theaforementioned calculations, predictive maintenance program 122identifies the features of drift score, volatility score, and anomalyscore from the non-defrost temperature signal.

Predictive maintenance program 122 generates additional features forinput into feature matrix X (step 216). In the example embodiment,predictive maintenance program 122 generates new features by applyingdifferent time series aggregation functions over time windows ofdiffering lengths in the past to the previously identified features offeature matrix X, i.e. drift score, volatility score, and anomaly score.The reason for utilizing various time windows is to more closelyidentify anomalous activity. For example, the mean of a metric may beconsistent over some length of time, e.g. two days, however over alonger duration, e.g. seven days, anomalous data may become moreapparent. This functionality allows for more sensitivity and preciseidentification of when a symptom manifests. In the example embodiment,for some score (Sc_(t)) on a given day (t) the corresponding time seriesaggregated score over k days in the past (ASc_(t,k)) is defined byEquation 5:

ASc _(t,k) =f(Sc _(t) ,Sc _(t−1) , . . . ,Sc _(t−k))  (5)

Here, f is any suitable aggregating function (e.g. mean, median, max,range, consecutive/non-consecutive days of metrics exceeding xpercentile, etc.) or combination thereof and k is the length of the timewindow in the past (e.g. k=5, 10, etc.). In the example embodiment,particular aggregating functions may be more suitable for certainscenarios than others. For example, a spike in temperature may be bestcaptured by a max function while consistently high temperatures may bebest captured by a mean or median function. In general, the aggregatingfunctions by which the features are measured are not limited to thosedescribed herein and may be customized to a particular refrigerationcase 110 or case type.

Predictive maintenance program 122 extracts case type and contentfeatures (step 218). In the example embodiment, case type and contentsare considered a categorical feature of feature matrix X becauserefrigeration case 110 can generally be classified into several types.For example, cases containing beverages are usually kept at a similartemperature, e.g. mid-thirties degrees Fahrenheit, while casescontaining frozen foods are typically kept below thirty-two degreesFahrenheit. Similarly, defrost set temperatures as well as defrost cyclefrequency are also consistent among similar cases and, accordingly, suchcase type characteristics may be incorporated as a feature into featurematrix X as a case type score.

Continuing at FIG. 3, predictive maintenance program 122 measuresobservation data (step 220). In the example embodiment, observationdata, i.e. observed data, are real time measurements of metrics used incalculating the aforementioned features of refrigeration case 110.Specifically, observation data is a representation of the current stateof refrigeration case 110 that is compared to the previously determinedtypical performance, i.e. signatures, in order to identify symptoms of afailure. In the example embodiment, predictive maintenance program 122measures observation data, such as an observational defrost temperaturetime series and observational non-defrost temperature time series, byreference to sensor 112 of refrigeration case 110. In other embodiments,predictive maintenance program 122 may make reference to or collectobservation data alternatively, for example by means used for collectingthe temperature time series at step 202 in FIG. 2A.

Predictive maintenance program 122 determines whether a failure symptomis identified (decision 222). The present invention may employ a varietyof methods using any one of the aforementioned features in order toidentify failure symptoms. Two of such methods are disclosed herein forillustrative purposes.

In one embodiment of the present invention administering a rule-basedframework (decision 222 continued), predictive maintenance program 122identifies failure symptoms by calculating a deviation of the observeddefrost data from the defrost temperature signature. In this particularembodiment, the defrost temperature signature is used as a baseline andobserved data exceeding the determined anomaly threshold relative to thedefrost signature is an indication of a failure symptom. In order tocompare the defrost temperature pattern with the observed data,predictive maintenance program 122 utilizes DTW. If predictivemaintenance program 122 determines that the observed defrost dataexceeds the anomaly threshold relative to the defrost temperaturesignature, predictive maintenance program 122 considers the dataanomalous and an indication of a failure symptom. In other embodiments,however, other statistical methods may be employed.

In another embodiment employing a machine learning framework (decision222 continued), predictive maintenance program 122 identifies failuresymptoms based on a feature matrix X that outputs a binary variable yindicating the absence (0) or presence (1) of a failure symptom. Therows of feature matrix X represent days of operation while the columnsrepresent refrigeration case 110 features calculated above, such asfeatures based on defrost temperatures (i.e. defrost anomaly score),features based on non-defrost temperatures (drift score, volatilityscore, and anomaly score), features derived by applying aggregatingfunctions to features, and features based on case type and case content.Features contained in the columns of feature matrix X are selectivelychosen to best capture the impact of some of the most probable symptomsleading to a failure in refrigeration case 110, including a faultydefrost profile, warming up of refrigeration case 110 that leads to anupward drift in temperature signal, increased variability in thetemperature signal, and a sudden change in the daily expectedtemperature profile over a very short period (e.g. 1-2 days). In theexample embodiment, predictive maintenance program 122 computes binaryoutcome variable y based on feature matrix X and an outcome of 1 is anindication that a symptom of a pending failure in refrigeration case 110has been identified. It will be appreciated that in other embodiments,more or less features and other calculations may be implemented inidentifying failure symptoms of refrigeration case 110.

In order to train predictive maintenance program 122 to use featurematrix X, predictive maintenance program 122 first loads previous data,including past failures and the measurements leading up to the failure,into the feature matrix (decision 222 continued). Predictive maintenanceprogram 122 then computes feature matrix X before, during, and after afailure in order to identify patterns and symptoms indicative of acurrent or future failure. Because the previously recorded failures maynot be accounted for (i.e. work order submitted) until long aftersymptoms of a failure have manifested and, importantly, because the dataleading up to a failure is the best predictor of a failure, predictivemaintenance program 122 considers the previous d amount of days leadingup to a recorded failure as having a failure symptom (i.e. binaryoutcome variable y=1). This method applies the presumption that althoughthe failure was likely not recorded until the day refrigeration case 110failed to reach set temperature, symptoms of the failure could have beenidentified d days prior to the recorded failure. In the exampleembodiment, d is defined as twenty-one days and thus the three weeksleading up to a recorded failure are marked as having symptoms of afailure in order to machine learn patterns indicative of a failuresymptom. Predictive maintenance program 122 computes feature matrix Xfor the days leading up to the recorded failure and derives a pattern offeature behaviour, as a whole, that may be used to predict futurefailures. A feature matrix X may be created for a variety ofrefrigeration cases 110 and a final feature matrix X may be computed byappending the feature matrices of all refrigeration cases.

If predictive maintenance program 122 does not identify a failuresymptom (decision 222 “NO” branch), then predictive maintenance program122 continues to measure observation data (step 220) and subsequentlyidentify failure symptoms (decision 222).

If predictive maintenance program 122 identifies a failure symptom(decision 222, “YES” branch), then predictive maintenance program 122diagnoses a root cause of the symptom or failure (step 224). In theexample embodiment, predictive maintenance program 122 diagnoses a rootcause of failure symptom using features including the non-defrost anddefrost features, previous labelled root cause data, process parametersof refrigeration case 110, and a type of refrigeration case 110. Forexample, predictive maintenance program 122 may reference rich labelleddata detailing previous failures in order to machine learn patternsassociated with the corresponding types of root causes. Based on thetype of symptom or failure detected, predictive maintenance program 122can reference past data, subject matter experts, or a combinationthereof in order to identify a typical root cause of the symptom orfailure, provide options/strategies to address it, and recommend thebest option based on associated costs and risks.

Predictive maintenance program 122 activates an alarm and opens a workorder corresponding to failure symptom or failure (step 226). In theexample embodiment, predictive maintenance program 122 activates analarm based on the confidence score exceeding a threshold amount.Moreover, predictive maintenance program 122 will additionally open awork order for the failure symptom or failure based on the confidencescore.

Predictive maintenance program 122 deactivates the alarm and closes thework order (step 228). Upon completion of the work order and resolutionof the failure symptom or failure, predictive maintenance program 122deactivates the alarm and closes the work order.

While the present invention has been described and illustrated withreference to particular embodiments, it will be appreciated by those ofordinary skill in the art that the invention lends itself to manydifferent variations not specifically illustrated herein.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

FIG. 3 illustrates a temperature time series of refrigeration case 110,in accordance with an embodiment of the present invention.

FIG. 4 illustrates expected and anomalous defrost cycles, in accordancewith an embodiment of the present invention.

FIG. 5 illustrates valid and invalid defrost cycles of refrigerationcase 110, in accordance with an embodiment of the present invention.

FIG. 6 illustrates a temperature time series having valid and invaliddefrost cycles based on refrigeration case 110 set temperature precedingand subsequent to defrosting, in accordance with an embodiment of thepresent invention.

FIG. 7 illustrates a homogenous cluster of defrost cycles, in accordancewith an embodiment of the present invention.

FIG. 8 illustrates a continuous cluster of defrost cycles, in accordancewith an embodiment of the present invention.

FIG. 9 illustrates a non-continuous cluster of defrost cycles, inaccordance with an embodiment of the present invention.

FIG. 10 illustrates a non-defrost temperature time series broken downinto seasonal, trend, and random components, in accordance with anembodiment of the present invention.

FIG. 11 depicts a block diagram of components of server 120 ofpredictive maintenance system 100 of FIG. 1, in accordance with anembodiment of the present invention. It should be appreciated that FIG.11 provides only an illustration of one implementation and does notimply any limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

Server 120 may include one or more processors 02, one or morecomputer-readable RAMs 04, one or more computer-readable ROMs 06, one ormore computer readable storage media 08, device drivers 12, read/writedrive or interface 14, network adapter or interface 16, allinterconnected over a communications fabric 18. Communications fabric 18may be implemented with any architecture designed for passing dataand/or control information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system.

One or more operating systems 10, and one or more application programs11, for example predictive maintenance program 122, are stored on one ormore of the computer readable storage media 08 for execution by one ormore of the processors 02 via one or more of the respective RAMs 04(which typically include cache memory). In the illustrated embodiment,each of the computer readable storage media 08 may be a magnetic diskstorage device of an internal hard drive, CD-ROM, DVD, memory stick,magnetic tape, magnetic disk, optical disk, a semiconductor storagedevice such as RAM, ROM, EPROM, flash memory or any othercomputer-readable tangible storage device that can store a computerprogram and digital information.

Server 120 may also include a R/W drive or interface 14 to read from andwrite to one or more portable computer readable storage media 26.Application programs 11 on said devices may be stored on one or more ofthe portable computer readable storage media 26, read via the respectiveR/W drive or interface 14 and loaded into the respective computerreadable storage media 08.

Server 120 may also include a network adapter or interface 16, such as aTCP/IP adapter card or wireless communication adapter (such as a 4Gwireless communication adapter using OFDMA technology). Applicationprograms 11 on said computing devices may be downloaded to the computingdevice from an external computer or external storage device via anetwork (for example, the Internet, a local area network or other widearea network or wireless network) and network adapter or interface 16.From the network adapter or interface 16, the programs may be loadedonto computer readable storage media 08. The network may comprise copperwires, optical fibers, wireless transmission, routers, firewalls,switches, gateway computers and/or edge servers.

Server 120 may also include a display screen 20, a keyboard or keypad22, and a computer mouse or touchpad 24. Device drivers 12 interface todisplay screen 20 for imaging, to keyboard or keypad 22, to computermouse or touchpad 24, and/or to display screen 20 for pressure sensingof alphanumeric character entry and user selections. The device drivers12, R/W drive or interface 14 and network adapter or interface 16 maycomprise hardware and software (stored on computer readable storagemedia 08 and/or ROM 06).

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

Based on the foregoing, a computer system, method, and computer programproduct have been disclosed. However, numerous modifications andsubstitutions can be made without deviating from the scope of thepresent invention. Therefore, the present invention has been disclosedby way of example and not limitation.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 12, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 40 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 40 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 12 are intended to be illustrative only and that computing nodes40 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 13, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 12) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 13 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and maintenance processing 96.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A computer-implemented method of predictingmaintenance for a refrigeration case, the method comprising: a computermeasuring real time temperature data of a refrigeration case; thecomputer identifying one or more defrost cycles within the measured realtime temperature data based on identifying one or more slopes of thereal time temperature data; the computer removing one or more anomalousdefrost cycles from the one or more defrost cycles in which a precedingnon-defrost cycle or a subsequent non-defrost cycle did not reach a settemperature; the computer removing one or more anomalous defrost cyclesfrom the one or more defrost cycles in which a defrost cycle ends with awork order; the computer identifying a defrost temperature signaturebased on a dominant cluster of the remaining one or more defrost cycles;the computer determining an anomaly threshold based on computing apercentile of a dynamic time warp distance of each of the one or moreremaining defrost cycles; the computer determining whether the real timetemperature data exceeds the anomaly threshold; based on determiningthat the real time temperature data exceeds the anomaly threshold, thecomputer identifying a failure symptom; based on identifying the failuresymptom, the computer identifying one or more possible root causes ofthe failure symptom based on previously labelled root cause data; thecomputer assigning a confidence score to each of the one or morepossible root causes; the computer raising an alarm if the confidencescore of any of the one or more possible root causes exceeds athreshold; the computer ranking the one or more possible root causesbased on the assigned confidence score; the computer opening a workorder corresponding to a possible root cause of the one or more possibleroot causes having a highest confidence score; and the computer closingthe work order based on determining that the possible root cause of thefailure symptom is rectified.